Generative Adversarial Network for Peptide Sequence Generation
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Solution Overview
Problem
Current computational tools lack the ability to generate new binding peptides with specified properties for MHC proteins, which are crucial for immune responses and transplant rejection, especially when the training dataset is imbalanced with fewer minority-class examples.
Innovation Solution
A generative adversarial network (GAN) is trained using both binding and non-binding peptide sequences to generate new peptides that mimic the training peptides, with a conditional Wasserstein GAN and dual-projection vectors to learn cross-entropy losses, enabling the generation of new binding peptides for MHC proteins associated with viruses or tumors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a standard GAN is used to generate peptide sequences, then new binding peptides can be generated, but the training dataset remains imbalanced with insufficient minority-class examples
Solution Approach 1:
The patent uses a generative adversarial network to copy the characteristics of existing binding peptide sequences and generate new synthetic binding peptides. The generator model learns the underlying distribution of binding peptides and creates realistic synthetic examples that mimic the properties of real binding sequences, thereby increasing the quantity of training data without requiring additional experimental validation of each generated peptide
Solution Approach 2:
The patent implements a feedback mechanism where the discriminator model evaluates generated peptide sequences and provides feedback to the generator. The discriminator is trained to distinguish between real and generated binding peptides, and this feedback loop continuously refines the generator's ability to produce high-quality binding peptide sequences that maintain accurate binding affinity characteristics
2Quantity of substance
If more minority-class examples are generated to balance the dataset, then the training data becomes more balanced, but the complexity of the generative model increases
Solution Approach 1:
The patent segments the generative task into distinct functional components: an encoder that processes input sequences, a generator that creates new peptide sequences, and a discriminator that evaluates authenticity. This segmentation allows each component to be optimized independently and simplifies the overall training process compared to a monolithic generative model
Solution Approach 2:
The patent inverts the traditional approach by training the discriminator first to recognize binding peptide characteristics, then using its feedback to guide the generator. Rather than directly optimizing the generator to produce binding peptides, the system uses the discriminator's evaluation capability to indirectly shape the generator's output quality, simplifying the generative process
3Loss of information
If computational tools predict binding interactions, then interaction scores can be calculated, but new binding peptides with specified properties cannot be generated
Solution Approach 1:
The patent creates a multi-functional system where the trained GAN model serves multiple purposes: it can predict binding interactions like traditional tools, generate new binding peptides with specified properties, and provide augmented training data for improving prediction accuracy. The same generator-discriminator architecture that balances the dataset also enables de novo peptide design with user-specified characteristics
Data Source
AI summary
Methods and systems for training a model include encoding training peptide sequences using an encoder model. A new peptide sequence is generated using a generator model. The encoder model, the generator model, and the discriminator model are trained to cause the generator model to generate new peptides that the discriminator mistakes for the training peptide sequences, including learning projection vectors with respective cross-entropy losses for binding sequences and non-binding sequences.